Comparer des méthodes

	Échantillonnage mobile d'expériences testé en pré-étude	Collecte de données par capteurs
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s (mobile ESM); pilot-testing practice codified in 2010s	1990s–2000s (widespread deployment with IoT ~2000s)
Auteur d'origine	Reed Larson & Mihaly Csikszentmihalyi (ESM); mobile adaptation developed across 2000s–2010s	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Type	Data collection technique	Quantitative / mixed data collection technique
Source fondatrice	Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social and Behavioral Science, 15, 41–56.	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Alias	pilot-tested mobile ESM, pretested mESM, validated mobile experience sampling, mobile ESM with pilot phase	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Apparentées	4	5
Résumé	Pilot-tested mobile experience sampling (mESM) is a data collection approach that combines smartphone-delivered, real-time self-report prompts — the Experience Sampling Method — with a structured pilot phase to validate the instrument, signal timing, burden level, and response quality before full deployment. The pilot phase is not optional decoration; it is the core quality gate that separates a rigorously validated mESM study from an ad hoc one.	Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.